Amin Hassani

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With the rapid scale out of supercomputers comes a corresponding higher failure frequency. Fault-tolerant methods have evolved to adapt to high rates of failure, but the behavior of MPI, the most widely used scalable programming middleware, is insufficient when confronting such failures. We present FA-MPI (Fault-Aware MPI), a set of extensions to the MPI(More)
In this paper, we consider cooperative node-specific direction-of-arrival (DOA) estimation in a fully connected wireless acoustic sensor network (WASN). We consider a scenario where each node is equipped with a local microphone array with a known geometry, but where the position of the nodes, as well as their relative geometry and hence the between-nodes(More)
In this paper, we study the effect of collaboration between nodes for direction of arrival (DOA) estimation in a full connected wireless acoustic sensor network (WASN) where the position of the nodes is unknown. Each node is equipped with a linear microphone array which defines a node-specific DOA with respect to a single common target speech source. We(More)
In this paper, we address the problem of distributed adaptive estimation of node-specific signals for signal enhancement or noise reduction in wireless sensor networks with multi-sensor nodes. The estimation is performed by a multi-channel Wiener filter (MWF) in which a low-rank approximation based on a generalized eigenvalue decomposition (GEVD) is(More)
We consider the design of a distributed algorithm that is suitable for a wireless acoustic sensor network formed by nodes solving multiple tasks (MDMT). In the network, some of the nodes aim at estimating the node-specific direction-of-arrival of some desired sources. Additionally, there are other nodes that aim at implementing either a multi-channel Wiener(More)
Many array-processing algorithms or applications require the estimation of a target signal subspace, e.g., for source localization or for signal enhancement. In wireless sensor networks, the straightforward estimation of a network-wide signal subspace would require a centralization of all the sensor signals to compute network-wide covariance matrices. In(More)
We consider a multi-task wireless sensor network (WSN) where some of the nodes aim at applying a multi-channel Wiener filter to denoise their local sensor signals, whereas others aim at implementing a linearly constrained minimum variance beamformer to extract node-specific desired signals and cancel interfering signals, and again others aim at estimating(More)
In this paper, we consider the problem of distributed estimation of node-specific signals in a fully-connected wireless sensor network with multi-sensor nodes. The estimation relies on a data-driven design of a spatial filter, referred to as the generalized eigenvalue decomposition (GEVD)-based multi-channel Wiener filter (MWF). In non-stationary or low-SNR(More)
In this paper, we present a distributed algorithm for network-wide signal subspace estimation in a fully-connected wireless sensor network with multi-sensor nodes. We consider scenarios where the noise field is spatially correlated between the nodes. Therefore, rather than an eigenvalue decomposition (EVD-) based approach, we apply a generalized EVD (GEVD-)(More)
The linearly constrained minimum variance (LCMV) beamformer has been widely employed to extract (a mixture of) multiple desired speech signals from a collection of microphone signals, which are also polluted by other interfering speech signals and noise components. In many practical applications, the LCMV beamformer requires that the subspace corresponding(More)